Sensing behavior change in chronic pain. A scoping review of sensor technology for use in daily life

Diego Vitali, Temitayo Olugbade, Christopher Eccleston, Edmund Keogh, Nadia Bianchi-Berthouze, Amanda Williams

Research output: Contribution to journalArticlepeer-review

3 Citations (SciVal)

Abstract

Technology offers possibilities for quantification of behaviors and physiological changes of relevance to chronic pain, using wearable sensors and devices suitable for data collection in daily life contexts. We conducted a scoping review of wearable and passive sensor technologies that sample data of psychological interest in chronic pain, including in social situations. Sixty articles met our criteria from the 2783 citations retrieved from searching. Three-quarters of recruited people were with chronic pain, mostly musculoskeletal, and the remainder with acute or episodic pain; those with chronic pain had a mean age of 43 (few studies sampled adolescents or children) and 60% were women. Thirty-seven studies were performed in laboratory or clinical settings and the remainder in daily life settings. Most used only 1 type of technology, with 76 sensor types overall. The commonest was accelerometry (mainly used in daily life contexts), followed by motion capture (mainly in laboratory settings), with a smaller number collecting autonomic activity, vocal signals, or brain activity. Subjective self-report provided “ground truth” for pain, mood, and other variables, but often at a different timescale from the automatically collected data, and many studies reported weak relationships between technological data and relevant psychological constructs, for instance, between fear of movement and muscle activity. There was relatively little discussion of practical issues: frequency of sampling, missing data for human or technological reasons, and the users' experience, particularly when users did not receive data in any form. We conclude the review with some suggestions for content and process of future studies in this field.
Original languageEnglish
Pages (from-to)1348-1360
Number of pages13
JournalPain
Volume165
Issue number6
Early online date2 Jan 2024
DOIs
Publication statusPublished - 1 Jun 2024

Data Availability Statement

There are no data associated with this manuscript.

Funding

This work was supported by a joint and equal investment from UKRI [grant number MR/W004151/1] and the charity Versus Arthritis [grant number 22891] through the Advanced Pain Discovery Platform (APDP) initiative. For UKRI, the initiative is led by the Medical Research Council (MRC), with support from the Biotechnology and Biological Sciences Research Council (BBSRC) and the Economic and Social Research Council (ESRC). The authors gratefully acknowledge the public contributors who shared their time, knowledge, and experiences enabling mutual learning and collaboration. No authors reported any conflicts of interest associated with this manuscript. The views expressed are those of the authors and not necessarily those of the Medical Research Council or Versus Arthritis.

FundersFunder number
Economic and Social Research Council
Medical Research Council
Biotechnology and Biological Sciences Research Council
Medical Research Council or Versus Arthritis
UK Research and InnovationMR/W004151/1
UK Research and Innovation
charity Versus Arthritis22891

Keywords

  • Automated data collection
  • Pain impact
  • Wearable technology

ASJC Scopus subject areas

  • Clinical Neurology
  • Neurology
  • Anesthesiology and Pain Medicine

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